We study bandit learning in matching markets with two-sided reward uncertainty, extending prior research primarily focused on single-sided uncertainty. Building on Irving's (1994) concept of super-stability and the partial-order proposal algorithm of Cseh and Juhos (2021), we use a simultaneous Extended Gale-Shapley procedure to certify stable matchings from incomplete preference information. By employing this procedure, our centralized algorithm attains logarithmic pessimal stable regret dependent on an instance-dependent admissible gap parameter. This algorithm is further adapted to a decentralized setting with a time-independent additive $O(N^3K)$ regret increase under the stated shared-flag communication model. Finally, we establish a centralized instance-dependent lower bound for binary stable regret on strict interior Bernoulli instances, elucidating the roles of admissible partial ranks and super-stable matching in characterizing the complexity of stable matching with bandit feedback.

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